@inproceedings{sung-kyle-2025-asc,
title = "{ASC} analyzer: A Python package for measuring argument structure construction usage in {E}nglish texts",
author = "Sung, Hakyung and
Kyle, Kristopher",
editor = "Bonial, Claire and
Torgbi, Melissa and
Weissweiler, Leonie and
Blodgett, Austin and
Beuls, Katrien and
Van Eecke, Paul and
Tayyar Madabushi, Harish",
booktitle = "Proceedings of the Second International Workshop on Construction Grammars and NLP",
month = sep,
year = "2025",
address = {D{\"u}sseldorf, Germany},
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.cxgsnlp-1.5/",
pages = "41--49",
ISBN = "979-8-89176-318-0",
abstract = "Argument structure constructions (ASCs) offer a theoretically grounded lens for analyzing second language (L2) proficiency, yet scalable and systematic tools for measuring their usage remain limited. This paper introduces the ASC analyzer, a publicly available Python package designed to address this gap. The analyzer automatically tags ASCs and computes 50 indices that capture diversity, proportion, frequency, and ASC-verb lemma association strength. To demonstrate its utility, we conduct both bivariate and multivariate analyses that examine the relationship between ASC-based indices and L2 writing scores."
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<abstract>Argument structure constructions (ASCs) offer a theoretically grounded lens for analyzing second language (L2) proficiency, yet scalable and systematic tools for measuring their usage remain limited. This paper introduces the ASC analyzer, a publicly available Python package designed to address this gap. The analyzer automatically tags ASCs and computes 50 indices that capture diversity, proportion, frequency, and ASC-verb lemma association strength. To demonstrate its utility, we conduct both bivariate and multivariate analyses that examine the relationship between ASC-based indices and L2 writing scores.</abstract>
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%0 Conference Proceedings
%T ASC analyzer: A Python package for measuring argument structure construction usage in English texts
%A Sung, Hakyung
%A Kyle, Kristopher
%Y Bonial, Claire
%Y Torgbi, Melissa
%Y Weissweiler, Leonie
%Y Blodgett, Austin
%Y Beuls, Katrien
%Y Van Eecke, Paul
%Y Tayyar Madabushi, Harish
%S Proceedings of the Second International Workshop on Construction Grammars and NLP
%D 2025
%8 September
%I Association for Computational Linguistics
%C Düsseldorf, Germany
%@ 979-8-89176-318-0
%F sung-kyle-2025-asc
%X Argument structure constructions (ASCs) offer a theoretically grounded lens for analyzing second language (L2) proficiency, yet scalable and systematic tools for measuring their usage remain limited. This paper introduces the ASC analyzer, a publicly available Python package designed to address this gap. The analyzer automatically tags ASCs and computes 50 indices that capture diversity, proportion, frequency, and ASC-verb lemma association strength. To demonstrate its utility, we conduct both bivariate and multivariate analyses that examine the relationship between ASC-based indices and L2 writing scores.
%U https://aclanthology.org/2025.cxgsnlp-1.5/
%P 41-49
Markdown (Informal)
[ASC analyzer: A Python package for measuring argument structure construction usage in English texts](https://aclanthology.org/2025.cxgsnlp-1.5/) (Sung & Kyle, CxGsNLP 2025)
ACL